A fast chromatic correlation clustering algorithm

2016 ◽  
Author(s):  
Jaishri Gothania ◽  
Bala Buksh
Keyword(s):  
Clustering Algorithm ◽  
Correlation Clustering

Content-Based Multimedia Retrieval Using Feature Correlation Clustering and Fusion

2013 ◽  
Vol 4 (2) ◽  
pp. 46-64 ◽  
Author(s):  
Hsin-Yu Ha ◽  
Fausto C. Fleites ◽  
Shu-Ching Chen
Keyword(s):  
Clustering Algorithm ◽  
Multiple Correspondence Analysis ◽  
Majority Vote ◽  
Multimedia Data ◽  
Multimedia Retrieval ◽  
Semantic Retrieval ◽  
Data Set ◽  
Correlation Clustering ◽  
Feature Correlation ◽  
Fusion Framework

Nowadays, only processing visual features is not enough for multimedia semantic retrieval due to the complexity of multimedia data, which usually involve a variety of modalities, e.g. graphics, text, speech, video, etc. It becomes crucial to fully utilize the correlation between each feature and the target concept, the feature correlation within modalities, and the feature correlation across modalities. In this paper, the authors propose a Feature Correlation Clustering-based Multi-Modality Fusion Framework (FCC-MMF) for multimedia semantic retrieval. Features from different modalities are combined into one feature set with the same representation via a normalization and discretization process. Within and across modalities, multiple correspondence analysis is utilized to obtain the correlation between feature-value pairs, which are then projected onto the two principal components. K-medoids algorithm, which is a widely used partitioned clustering algorithm, is selected to minimize the Euclidean distance within the resulted clusters and produce high intra-correlated feature-value pair clusters. Majority vote is applied to subsequently decide which cluster each feature belongs to. Once the feature clusters are formed, one classifier is built and trained for each cluster. The correlation and confidence of each classifier are considered while fusing the classification scores, and mean average precision is used to evaluate the final ranked classification scores. Finally, the proposed framework is applied on NUS-wide Lite data set to demonstrate the effectiveness in multimedia semantic retrieval.

scholarly journals SPCA Assisted Correlation Clustering of Hyperspectral Imagery

2014 ◽  
Vol II-8 ◽  
pp. 111-116 ◽  
Author(s):  
A. Mehta ◽  
O. Dikshit
Keyword(s):  
Principal Component Analysis ◽  
Clustering Algorithm ◽  
Analysis Of Algorithms ◽  
Hyperspectral Imagery ◽  
Principal Component ◽  
Unsupervised Classification ◽  
Component Analysis ◽  
Feature Reduction ◽  
Correlation Clustering ◽  
Selection Of

In this study, correlation clustering is introduced to hyperspectral imagery for unsupervised classification. The main advantage of correlation clustering lies in its ability to simultaneously perform feature reduction and clustering. This algorithm also allows selection of different sets of features for different clusters. This framework provides an effective way to address the issues associated with the high dimensionality of the data. ORCLUS, a correlation clustering algorithm, is implemented and enhanced by making use of segmented principal component analysis (SPCA) instead of principal component analysis (PCA). Further, original implementation of ORCLUS makes use of eigenvectors corresponding to smallest eigenvalues whereas in this study eigenvectors corresponding to maximum eigenvalues are used, as traditionally done when PCA is used as feature reduction tool. Experiments are conducted on three real hyperspectral images. Preliminary analysis of algorithms on real hyperspectral imagery shows ORCLUS is able to produce acceptable results.

scholarly journals Bi-correlation clustering algorithm for determining a set of co-regulated genes

2009 ◽  
Vol 25 (21) ◽  
pp. 2795-2801 ◽  
Author(s):  
Anindya Bhattacharya ◽  
Rajat K. De
Keyword(s):  
Clustering Algorithm ◽  
Correlation Clustering

scholarly journals A Distributed GPU-based Correlation Clustering Algorithm for Large-scale Signed Social Networks

2017 ◽  
Author(s):  
Mario Levorato ◽  
Lúcia Drummond ◽  
Rosa Figueiredo ◽  
Yuri Frota
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
Large Network ◽  
Correlation Clustering ◽  
Signed Networks ◽  
Parallel Solution ◽  
Process Networks ◽  
Novel Approach ◽  
Signed Social Networks ◽  
Solution Methods

When applied to signed networks, the Correlation Clustering (CC) problem consists of an important tool to study how balanced a social group behaves and if this group might evolve to a possible balanced state. Solving such combinatorial optimization problem is a challenging task, which heavily relies on heuristic procedures, one of the few solution methods capable of analyzing large network instances. In this work, we present a novel approach to solve the CC problem on large-scale signed networks. A distributed GPU-powered version of the ILS metaheuristic, which benefits from data parallelism, has been developed. This technique provides good quality clustering results when compared to non-distributed methods. Experiments were conducted on both synthetic and real datasets. The proposed algorithm achieved improved solution values when compared to the existing parallel solution method. In particular, one of the largest graphs we have considered in our experiments contains 1 million nodes and 8 million edges – such graph can be clustered in two hours using our algorithm. The new method can process networks for which there is no efficient solution using the existing algorithms found in the literature.

Distributed Entropy Energy-Efficient Clustering algorithm for cluster head selection (DEEEC)

2020 ◽  
Vol 39 (6) ◽  
pp. 8139-8147
Author(s):  
Ranganathan Arun ◽  
Rangaswamy Balamurugan
Keyword(s):  
Energy Efficient ◽  
Clustering Algorithm ◽  
Cluster Head ◽  
Residual Energy ◽  
Energy Utilization ◽  
Sensor Nodes ◽  
Second Stage ◽  
Energy Efficient Clustering ◽  
Two Stages ◽  
Ch Selection

In Wireless Sensor Networks (WSN) the energy of Sensor nodes is not certainly sufficient. In order to optimize the endurance of WSN, it is essential to minimize the utilization of energy. Head of group or Cluster Head (CH) is an eminent method to develop the endurance of WSN that aggregates the WSN with higher energy. CH for intra-cluster and inter-cluster communication becomes dependent. For complete, in WSN, the Energy level of CH extends its life of cluster. While evolving cluster algorithms, the complicated job is to identify the energy utilization amount of heterogeneous WSNs. Based on Chaotic Firefly Algorithm CH (CFACH) selection, the formulated work is named “Novel Distributed Entropy Energy-Efficient Clustering Algorithm”, in short, DEEEC for HWSNs. The formulated DEEEC Algorithm, which is a CH, has two main stages. In the first stage, the identification of temporary CHs along with its entropy value is found using the correlative measure of residual and original energy. Along with this, in the clustering algorithm, the rotating epoch and its entropy value must be predicted automatically by its sensor nodes. In the second stage, if any member in the cluster having larger residual energy, shall modify the temporary CHs in the direction of the deciding set. The target of the nodes with large energy has the probability to be CHs which is determined by the above two stages meant for CH selection. The MATLAB is required to simulate the DEEEC Algorithm. The simulated results of the formulated DEEEC Algorithm produce good results with respect to the energy and increased lifetime when it is correlated with the current traditional clustering protocols being used in the Heterogeneous WSNs.

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

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